The deployment of high quality imaging to smart phones, digital cameras, personal digital assistants (PDA), and other information devices with screens has grown tremendously in recent years. The wide variety of information devices supporting image processing and text recognition requires the ability to process multiple types of images with varying degrees of available text information.
Imaging devices with optical character recognition (OCR) can employ a variety of techniques for recognizing text. Some OCR systems can extract textual information from structured documents where the location of text in the image can be predicted. Other OCR systems can extract text from images having simple, uncluttered backgrounds where the text can be readily identified. Such systems are processing information in images of varying quality, resolution, and orientation, but rely on additional text cues such as regular spacing, orientation, and fonts to assist in text detection.
Thus, a need still remains for an image processing system that can deliver good picture quality and features across a wide range of device with different sizes, resolutions, and image quality. In view of the increasing demand for providing optical character recognition on the growing spectrum of intelligent imaging devices, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is critical that answers be found for these problems. Additionally, the need to save costs, improve efficiencies and performance, and meet competitive pressures, adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have long been sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.